Dan Chitwood

Dan Chitwood

Assistant Professor, Department of Horticulture; Department of Computational Mathematics, Science and Engineering
Room A322, Plant and Soil Science Building
  1066 Bogue St.
 (517) 353-0462

Post-doctoral researcher (2009-2013), University of California, Davis 

Ph.D in Biological Sciences (2004-2009), Cold Spring Harbor Laboratory

B.S. in Genetics (1999-2003), University of California, Davis


Structure---whether inorganic matter, the morphology of living things, or objects crafted by culture---is data rich. If the information contained within a single material, organism, or cultural artifact is immense, the collective information contained in the universe of things is fathomless. 

X-Ray Computed Tomography (CT) creates reconstructions of objects down to micron resolution, but a method to quantify and summarize the shape and structure of these exquisite objects is lacking. The emerging field of Topological Data Analysis (TDA) is a collection of tools from pure mathematics that quantifies the shape of data. These tools include persistent homology, which gives an algebraic descriptor summarizing changes in structure over a changing parameter, and the mapper graph, which provides a skeletonization of structure through the lens of a chosen filtration function. Different filtration functions highlight varying features in data.

We will be using X-ray CT and TDA to quantify shapes in nature, not least of which, plants. Evolution, domestication, and environment sculpt the morphology of living organisms. Embedded in organismal morphology is information, about the genes that increase yield in crops, have been modulated by evolution to create the spectacular diversity in plants, or features that can be used as a proxy to measure the effects of environment and climate change on plants. There is also the chance to change our perspective of the plant phenotype: rather than a series of shapes that develop through time, it is also possible to describe the plant form as a single, 4D shape. We will be using X-ray CT and analysis using TDA to explore plant morphology, development, plasticity, and to innovate new ways of thinking about, describing, quantifying, and using shape information in the plant sciences and beyond.

Research Website: https://dhchitwood.wixsite.com/morphologylab

The labs of Dr. Elizabeth Munch (http://elizabethmunch.com/) and Dr. Dan Chitwood (https://dhchitwood.wixsite.com/morphologylab) in the Department of Computational Mathematics, Science & Engineering at Michigan State University invite applications for a Research Associate-Fixed Term (Postdoctoral Researcher). The successful candidate will have previous experience in applying Topological Data Analysis (TDA) to scientific questions and working productively with mathematicians and computer scientists, as well as domain scientists without mathematical training. A focus of the research the candidate will undertake will be using TDA to quantify 3D, voxel-based images collected using X-ray Computed Tomography. The research team the candidate will join focuses on applying TDA to models of plant morphology, but the candidate will also work broadly across disparate fields of science, and a collaborative, diplomatic, team-oriented personality is a prerequisite for this position. Enthusiasm for education and outreach is a must, as in addition to publishing results, the candidate will disseminate TDA methods, code, data, and resources they develop to the scientific community in a reproducible, open, and innovative way. Above all, the candidate will have a passion for applying TDA approaches in the sciences and working with scientists in a productive manner to arrive at a working theory of applied topology in the sciences.

[1]   Li M, An H, Angelovici R, Bagaza C, Batushansky A, Clark L, Coneva V, Donoghue M, Edwards E, Fajardo D, Fang H, Frank M, Gallaher T, Gebken S, Hill T, Jansky S, Kaur B, Klahs P, Klein L, Kuraparthy V, Londo J, Migicovsky Z, Miller A, Mohn R, Myles S, Otoni W, Pires JC, Riffer E, Schmerler S, Spriggs E, Topp C, Van Deynze A, Zhang K, Zhu L, Zink BM, Chitwood DH (2018) Topological Data Analysis as a Morphometric Method: Using Persistent Homology to Demarcate a Leaf Morphospace. Front Plant Sci. 9:553. DOI: https://doi.org/10.3389/fpls.2018.00553

[2]   Bucksch A, Atta-Boateng A, Azihou AF, Battogtokh D, Baumgartner A, Binder BM, Braybrook SA, Chang C, Coneva V, DeWitt TJ, Fletcher AG, Gehan MA, Diaz-Martinez DH, Hong L, Iyer-Pascuzzi AS, Klein LL, Leiboff S, Li M, Lynch JP, Maizel A, Maloof JN, Markelz RJC, Martinez CC, Miller LA, Mio W, Palubicki W, Poorter H, Pradal C, Price CA, Puttonen E, Reese J, Rellán-Álvarez R, Spalding EP, Sparks EE, Topp CN, Williams J, Chitwood DH (2017) Morphological plant modeling: Unleashing geometric and topological potential within the plant sciences.Front Plant Sci. 8:900. DOI: https://doi.org/10.3389/fpls.2017.00900

[3]   Li M, Duncan K, Topp CN, Chitwood DH (2017) Persistent homology and the    branching topologies of plants. Am J Bot. 104(3):349-353. DOI: http://dx.doi.org/10.3732/ajb.1700046